Overview
ModelBest (面壁智能) together with the OpenBMB community released ForgeStencil on August 4, positioning it as the first open-source AI system that automates research and deployment of Stencil computation optimization. Stencil is a fundamental and compute-intensive pattern in scientific computing and industrial simulation, underlying weather modeling, seismic exploration, electromagnetic simulation, and computational fluid dynamics. It is highly sensitive to memory bandwidth and typically dominates application runtime.
How It Works
ForgeStencil fully automates the chain of *find bottleneck → write code → verify → integrate*:
- KernelAgent ("writes code"): automatically synthesizes high-performance compute kernels that approach hardware limits, tailored to a given scenario and hardware.
- AppAgent ("installs software"): analyzes real applications, locates hot spots, verifies correctness, and integrates kernels back into the original software seamlessly.
- hypre: 3.86x
- minisweep (reactor neutron transport): 5.78x
- gprMax / FDTD electromagnetic simulation: 2.47x
- RTM oil & gas seismic imaging: 1.81x
- QuantLib bond pricing: 1.82x
- fp32 geometric mean: 2.35x
- fp16 additional: 1.95x
- Variable-coefficient Stencil (the hardest class): 1.34x
- Speedups come from vendor-run end-to-end evaluation on the GPU implementation bundled with each application; full baseline and hardware configuration details are not fully disclosed, and independent third-party reproduction is still early.
- Coverage is limited to the Stencil class of hot spots; this is not a general-purpose code optimizer.
- Comparisons such as "the work of 8 engineers for nearly a year, finished in 7 days" are media translations and should not be treated as precise benchmarks.
- Source repository: https://github.com/OpenBMB/ForgeStencil (open-sourced globally; license and full benchmark set should be confirmed in the repo)
- ModelBest: https://modelbest.cn
- Xinhua Finance report: https://www.eeo.com.cn/2026/0804/986209.shtml
- 163 Industry coverage: https://www.163.com/dy/article/L3GLM6PM053179F1.html
- Dual-agent design: KernelAgent for kernel synthesis, AppAgent for application-level integration.
- Focus on Stencil, a memory-bandwidth-bound pattern central to many industrial simulations.
- End-to-end median speedup 1.41x across 100+ real applications; individual apps up to 5.78x.
- Kernel-level geometric mean 2.35x fp32 and 1.95x fp16 over leading open baselines.
- Positions HPC tuning as a scalable, automated pipeline rather than an expert-driven manual process.
Users only provide source code; the system handles the rest with zero manual intervention.
Reported Results
Within one week, ForgeStencil processed 100+ real scientific and industrial software packages, roughly 42% of which map directly to real production scenarios.
End-to-end acceleration (median): 1.41x.
Notable application-level speedups:
At the kernel level, same-hardware comparison against top open-source baselines (Halide, Devito, EBISU, DRStencil, FlashFFTStencil):
Significance
The work is positioned as different from typical AI coding: instead of generating functional code, it generates high-performance code that approaches the physical limits of the hardware and deploys it autonomously. It reframes HPC performance tuning from an individual-expert craft into a parallel, reproducible pipeline. ModelBest places this in the ForgeEngineering paradigm, building on the May release of ForgeTrain ("AI makes AI"). Shared agent knowledge bases and real-time experience synchronization are cited as advantages over human-only knowledge sharing. Reported time-to-optimize an application drops from weeks to hours, effectively raising R&D throughput by roughly two orders of magnitude, with the workflow scaling with available compute.